76 research outputs found
Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games
In freemium games, the revenue from a player comes from the in-app purchases
made and the advertisement to which that player is exposed. The longer a player
is playing the game, the higher will be the chances that he or she will
generate a revenue within the game. Within this scenario, it is extremely
important to be able to detect promptly when a player is about to quit playing
(churn) in order to react and attempt to retain the player within the game,
thus prolonging his or her game lifetime. In this article we investigate how to
improve the current state-of-the-art in churn prediction by combining
sequential and aggregate data using different neural network architectures. The
results of the comparative analysis show that the combination of the two data
types grants an improvement in the prediction accuracy over predictors based on
either purely sequential or purely aggregated data
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